Metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis as a tool for biodiversity informatics: A case study on the classification of Rosa x damascena

Metabolomics is the comprehensive and simultaneous identification and quantification of metabolites in living cells. The term metabolome is used to describe the observable chemical profile or fingerprint of the metabolites in a whole tissue. Although being a new approach to study natural compounds, metabolomics uses traditional analytical techniques, including extraction methods, which can be followed by nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Although metabolomics has been successfully applied to quality control issues, the examples of its use for chemosystematics are few. Thus, the analysis of four taxa of Rosa x damascena (R. damascena Mill., R. damascena semperflorens, R. damascena trigintipetala and R. duchesse of Portland) was carried out by NMR spectroscopy as a tool for their classification. A principal component analysis of the 1H NMR spectra, based on the metabolites found in organic and aqueous fractions, showed a clear similarity of the samples. In particular, the major contributions from the aqueous fraction, preliminarily considered as a biomarker of R. x damascena group, are the flavonoids kaempferol and quercetin, glycosilated with glucose and rhamnose units. Our analysis demonstrated a close chemotaxonomic correlation among the four taxa, making this method a reliable tool for chemosystematics.